The analysis of protein complexes and interaction networks, and their dynamic behavior are of central importance in biological research. Affinity purification coupled with mass spectrometry (AP-MS) is now widely used for protein interaction analysis. Our work addresses the critical need to develop robust computational methods and tools for these data. We have demonstrated the great utility of label-free quantitative protein abundance information that can be extracted from AP-MS data, and developed the Statistical Analysis of INTeractomes (SAINT) framework for scoring protein interactions in AP-MS studies. We have also initiated an international consortium to comprehensively catalogue the non-specific binding proteins observed in AP-MS experiments - the Contaminant Repository for Affinity Purification (CRAPome.org). Building upon these advances, we will continue toward our goal of developing a comprehensive computational resource for scoring protein interaction data applicable to most commonly used experimental protocols and MS platforms. We will also gain a better understanding of the underlying mechanisms of non-specific binding - generating knowledge useful both for retrospective analysis of previously published data and for the design of future experiments. By integrating the experimental AP-MS data with external information such as structure-based protein interaction predictions, we will further improve the sensitivity of detection of low abundance and transient interactions. It has also become apparent that charting a complete interaction map for an organism like human is a community-wide effort, with multiple groups contributing separate portions of the entire interactome. We will develop a novel computational framework for consistent integration of AP-MS datasets from different studies, leading to more complete and accurate quantitative interaction networks. Lastly, one important problem that has yet to be fully addressed is the quantitative analysis protein complexes and interaction networks dynamics. The emergence of highly sensitive multiplex MS techniques presents such an opportunity, and we will develop advanced computational algorithms and tools for differential and dynamic interactome analysis using multiplex MS data. We will continue providing our widely used computational tools and data resources to the biological community, along with benchmark datasets for further development of computational methods by other scientists.